Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel

Change detection can be performed in a supervised manner. However, supervised methods for synthetic aperture radar (SAR) image change detection may suffer from lack of training samples. Therefore, in this letter, a semisupervised support vector machine classifier based on a cluster-neighborhood (CN) kernel is proposed for SAR image change detection. In the proposed method, samples are categorized into two neighborhoods with kernel k-means clustering algorithm. In addition, a CN kernel is constructed based on the composite-ratio kernel using the neighborhood-based statistical features. When a few labeled samples are available, the proposed CN kernel explores the information of unlabeled samples to enhance its discriminative ability and enhance its robustness against speckles. Experimental results on real SAR image change detection demonstrate the effectiveness of the proposed method when a few labeled samples are available.